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Data Processing, Algorithm and Modelling
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Extraction of linear features from vehicle-borne laser data
- Image Creation and Analysis
Raster image is created from point cloud laser data.
A blank grid is defined with equal height and width
grid size. The grid size is fixed at 20cmx20cm. It is
not necessary to keep the square grid size. The grid
size can be varied based on the laser scanner’s
along- track resolution (distance between the
successive scan lines). We have found that 20cm
grid is effective for our data. The size (height and
width) of the blank grid is defined by the extents of
the x and y coordinates of the laser data. Z-coordinate
represents the height data. After, defining
the blank grid, the laser data are projected on the
horizontal plane (x- y plane). We can create different
types of images while projecting the laser points on
the grid, e.g. density image, maximum height image
or average image. Density image shows the number
of laser points falling on each grid. This is simply the
count of the laser points falling on each grid. Linear
features like guardrails, and cables exhibit very low
value on this image. Maximum or minimum height
image shows the maximum or minimum height value
of each grid. This is created by computing the
maximum or minimum height of all the points falling
on each grid. Building faces will exhibit higher grid
value on maximum height image, where as
guardrails exhibit lower value on maximum height
image as they appear at lower height compared to
the building (roof edge of the building). Average
image is created by computing the average height
value of all the laser points falling on each grid.
Density image and maximum height images are
created for visualization purpose to show the
appearance of different features when such images
are created from laser point cloud data. Figure 2
Figure 2: Road and non-road Classified Laser
Points. Red – Road Points Blue – non- road
points
Figure 3: Density Image (Number of Laser Points per Grid).
shows the classified road and non- road laser point data. The road data are shown in red color points and
non-road data are shown in blue color points.
Figure 4: Maximum Height Image
Figure 5: Binary Image overlaid with straight lines from radon transformation.
- Binary Image Creation
Binary image is created by filtering the image using maximum and minimum height threshold values.
This is set based on the definition of the guardrail. Guardrails are assumed to be about one meter higher
from the road surface. The height value of each laser point is normalized before creating the image. The
normalization is done by making the road surface height equal to zero. Thus any point that is at a height
of one meter from the road surface will have height value one meter. The guardrails generally appear
along the roadsides or the road as well to separate the driving lanes. Normally, guardrails have height of
about one meter. Thus we set maximum height threshold value of 1.2m and minimum height threshold of
0.2m. By setting these threshold values, we will be sel ecting the grids on the image that have values
from 0.2m to 1.2m. By changing these threshold values other linear features (like cables) can also be
identified, though they need further analysis. Figure 5 shows the binary image. We can see at least two
linear features (guard rails) clearly and the third one is also seen but it is not continuous as the other two.
- Line Detection by Radon Transformation
Radon transformation is used to detect the lines on the binary image. Hough Transformation is another
alternative approach. We have assumed that linear features resemble straight lines rather that the
curved ones.
The radon transform represents an image as a collection of projections along various directions.
Projections can be computed along any angle ž. In general, the Radon transform of f(x,y) is the line
integral off parallel to the y´ axis. It is given by equations 1 and 2.
However, radon transform simply provides the direction where the straight lines appear. Thus it is not
possible to know the actual length of the line segment. It is also not possible to identify the individual
lines if the lines fall on the same direction. Thus we select the prominent peaks from the radon image as
seed line direction. These seed points or lines are further used to identify the actual lines on the image.
The selection of peaks from the radon image is done by morphological operation on the radon image.
The morphological operation involves, dilation using structuring line elements and threshold value (of
radon space).
Figure 6 shows radon transform of the binary image shown in figure 5. Figure 7 shows the result of
morphological operation of radon image to select only the peak values. These peak values are taken as
the orientation of major linear features on the image. The peaks thus identified are used to generate
candidate straight lines. These straight lines are plotted over the binary image as shown in figure 5.
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